De la escasez de recursos al laboratorio digital de materiales: la IA generativa y el diseño orientado a competencias en la formación de intérpretes de conferencias
DOI:
https://doi.org/10.24310/redit.20.2026.23688Palabras clave:
IA generativa, Formación de intérpretes, Carga cognitiva, Diseño de materiales, Ingeniería pedagógica, Generación aumentada por recuperación, Pedagogía orientada a destrezasResumen
La formación de intérpretes de conferencias ha estado durante mucho tiempo limitada por una escasez crónica de materiales validados pedagógicamente y orientados a destrezas específicas. Los repositorios existentes, como el de discursos de las Naciones Unidas y la Unión Europea, aunque son muy útiles, no fueron concebidos para un desarrollo progresivo de las destrezas interpretativas ni calibrados según las complejas exigencias cognitivas identificadas por los Modelos de Esfuerzo de Gile (1995, 2009). Este artículo propone un marco conceptual y pedagógico —el Laboratorio Digital de Materiales (MDL)— en el que se emplean herramientas de inteligencia artificial generativa (GenAI) para diseñar materiales formativos alineados con objetivos cognitivos y lingüísticos específicos. En vez de seleccionar discursos de repositorios fijos, los formadores pueden operar en un entorno de diseño dinámico, cuyos contenidos generados por IA se basen en fuentes de información fiables, mediante Generación Aumentada por Recuperación (RAG) con NotebookLM, se sinteticen en forma de audio a través de las utilidades de conversión de texto a voz de Google AI Studio y se articulen mediante ingeniería de prompts basada en Gemini. Este marco se apoya en una síntesis teórica de los Modelos de Esfuerzo de Gile, la Teoría de la Carga Cognitiva y la pedagogía orientada a destrezas, para proponer que las subdestrezas interpretativas —escucha y análisis, memoria, producción y coordinación— deben funcionar como parámetros de diseño y no como resultados incidentales de una práctica basada en la mera exposición. Dos estudios de caso ilustrativos, que simulan una conferencia sobre adaptación al cambio climático y un foro de política económica, demuestran la viabilidad operativa y la precisión pedagógica de la propuesta. Este artículo realiza tres contribuciones principales: una reteorización de las destrezas interpretativas como objetivos pedagógicos diseñables, un flujo de trabajo prototípico en cinco fases para la generación de materiales con IA y un marco de competencias revisado para el formador de intérpretes como ingeniero pedagógico. La validación empírica futura de los resultados de aprendizaje se identifica como el siguiente paso principal en la agenda de investigación.
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Adamakis, M., & Rachiotis, T. (2025). Artificial intelligence in higher education: A state-of-the-art overview of pedagogical integrity, artificial intelligence literacy, and policy integration. Encyclopedia, 5(1), 180. https://doi.org/10.3390/encyclopedia5040180
Al-Suhaim, D. S. (2024). Exploring theoretical dimensions in interpreting studies: A comprehensive overview. Arab World English Journal for Translation & Literary Studies, 8(1), 15-43.
Bakar, S., & Tapsoba, R. (2026). Artificial intelligence in classroom teaching: Prospects, challenges and framework for responsibly orchestrated mediation. Social Science Chronicle, 6(1), 1-21. https://doi.org/10.56106/ssc.2026.002
Cai, R., Dong, Y., Zhao, N., & Lin, J. (2015). Factors contributing to individual differences in the development of consecutive interpreting competence for beginner student interpreters. The Interpreter and Translator Trainer, 9(1), 104-120.
Carrasco-Sáez, J. L., Contreras-Saavedra, C., San-Martín-Quiroga, S., Contreras-Saavedra, C. E., & Viveros-Muñoz, R. (2025). Analyzing higher education students’ prompting techniques and their impact on ChatGPT’s performance: An exploratory study in Spanish. Applied Sciences, 15(7651). https://doi.org/10.3390/app15147651
Chan, C. H. Y. (2013). From self-interpreting to real interpreting: A new web-based exercise to launch effective interpreting training. Perspectives: Studies in Translatology, 21(3), 358-377. https://doi.org/10.1080/0907676X.2012.657654
Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20(38). https://doi.org/10.1186/s41239-023-00408-3
Chan, V. (2023). Investigating the impact of a virtual reality mobile application on learners’ interpreting competence. Journal of Computer Assisted Learning, 1-17. https://doi.org/10.1111/jcal.12796
Chang, C.-C., & Wu, M. M.-C. (2017). From conference venue to classroom: The use of guided conference observation to enhance interpreter training. The Interpreter and Translator Trainer, 11(1), 21-42. https://doi.org/10.1080/1750399X.2017.1359759
Conde, J. M., & Chouc, F. (2019). Multilingual mock conferences: A valuable tool in the training of conference interpreters. The Interpreters’ Newsletter, 24, 1-17.
Cordero, J., Torres-Zambrano, J., & Cordero-Castillo, A. (2025). Integration of generative artificial intelligence in higher education: Best practices. Education Sciences, 15(1), 32. https://doi.org/10.3390/educsci15010032
Corpas Pastor, G. (2018). Tools for interpreters: The challenges that lie ahead. Current Trends in Translation Teaching and Learning E, 5, 157-182.
Corpas Pastor, G. (2020). Language technology for interpreters: The VIP Project. Proceedings of the 42nd Conference “Translating and the Computer” (TC42).
Cui, F., Li, D., & Zhuang, C. (2025). Introduction: Transforming translation education through artificial intelligence. The Interpreter and Translator Trainer, 19(3-4), 227-233. https://doi.org/10.1080/1750399X.2025.2561258
Djovcos, M., Klabal, O., & Sveda, P. (2023). Training interpreters: Old and new challenges. Bridge: Trends and Traditions in Translation and Interpreting Studies, 4(1), 1-12.
Fan, D. (2012). The development of expertise in interpreting through self-regulated learning for trainee interpreters [Doctoral dissertation]. University of Newcastle upon Tyne.
Frittella, F. M. (2021). Computer-assisted conference interpreter training: Limitations and future directions. Journal of Translation Studies, 2(2021), 103-142. https://doi.org/10.3726/JTS022021.6
Garcia-Penalvo, F. J. (2023). Generative artificial intelligence: Open challenges, opportunities, and risks in higher education. CEUR Workshop Proceedings, 3696, 4-15.
Gile, D. (1995). Basic concepts and models for interpreter and translator training. John Benjamins.
Gile, D. (1999). Testing the Effort Models’ tightrope hypothesis in simultaneous interpreting: A contribution. Hermes, Journal of Linguistics, 23, 153-172.
Gile, D. (2009). Basic concepts and models for interpreter and translator training (Rev. ed.). John Benjamins.
Gile, D. (2021). The Effort Models of interpreting as a didactic construct. In R. Munoz Martin et al. (Eds.), Advances in cognitive translation studies (pp. 139-153). Springer Nature Singapore.
Hatiarová, P. (2025). AI in interpreting training. L10N Journal, 1(4), 45-66.
Kalina, S. (2000). Interpreting competences as a basis and a goal for teaching. Fachhochschule Koln.
Kasneci, E., Sessler, K., Kuchemann, S., et al. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.
Li, X. (2015a). Mock conference as a situated learning activity in interpreter training: A case study of its design and effect as perceived by trainee interpreters. The Interpreter and Translator Trainer, 9(3), 323-341.
Li, X. (2015b). Putting interpreting strategies in their place: Justifications for teaching strategies in interpreter training. Babel, 61(2), 170-192. https://doi.org/10.1075/babel.61.2.02li
Li, X. (2018). Material development principles in undergraduate translator and interpreter training: Balancing between professional realism and classroom realism. The Interpreter and Translator Trainer, 12(4), 369-389.
Macnamara, B. N., Moore, A. B., Kegl, J. A., & Conway, A. R. A. (2011). Domain-general cognitive abilities and simultaneous interpreting skill. Interpreting, 13(1), 121-142.
Moser-Mercer, B. (2000/01). Simultaneous interpreting: Cognitive potential and limitations. Interpreting, 5(2), 83-94.
Moukatib, M., & Ben Seddik, A. (2026). The role of AI in translator training: Assessing AI’s influence on translation education and professional training. International Journal of Linguistics and Translation Studies, 7(1), 123-143. https://doi.org/10.36892/ijlts.v7i1.669
Munoz-Basols, J., Neville, C., Lafford, B. A., & Godev, C. (2023). Potentialities of applied translation for language learning in the era of artificial intelligence. Hispania, 106(2), 171-194.
Parrilla Gomez, L., & Postigo Pinazo, E. (2025). Artificial intelligence in the training of public service interpreters. Language & Communication, 103, 86-107.
Purba, S. W. D., Silitonga, B. N., & Yang, J. J. (2025). AI-assisted learning: A systematic review. Turkish Online Journal of Distance Education (TOJDE), 26(4), 77-94.
Qian, Y. (2025). Pedagogical applications of generative AI in higher education: A systematic review of the field. TechTrends, 69, 1105-1120.
Rybina, N. V., Koshil, N. Ye., & Hyryla, O. S. (2025). Artificial intelligence and translation in English language teaching: Opportunities and challenges. Medychna osvita [Medical Education], (2), 87-91. https://doi.org/10.11603/m.2414-5998.2025.2.15494
Sachtleben, A. (2015). Pedagogy for the multilingual classroom: Interpreting education. Translation & Interpreting: The International Journal of Translation and Interpreting Research, 7(2), 51-59.
Sandrelli, A., & de Manuel Jerez, J. (2007). The impact of information and communication technology on interpreter training: State-of-the-art and future prospects. The Interpreter and Translator Trainer, 1(2), 269-303.
Seeber, K. G. (2011). Cognitive load in simultaneous interpreting: Existing theories—New models. Interpreting, 13(2), 176-204. https://doi.org/10.1075/intp.13.2.02see
Seeber, K. G., & Arbona, E. (2020). What’s load got to do with it? A cognitive-ergonomic training model of simultaneous interpreting. The Interpreter and Translator Trainer, 14(3), 1-18. https://doi.org/10.1080/1750399X.2020.1839996
Seeber, K. G., & Kerzel, D. (2012). Cognitive load in simultaneous interpreting: Model meets data. International Journal of Bilingualism, 16(2), 228-242.
Serra, P., & Oliveira, A. (2025). AI-powered prompt engineering for Education 4.0: Transforming digital resources into engaging learning experiences. Education Sciences, 15(12), 1640. https://doi.org/10.3390/educsci15121640
Shahzad, T., Mazhar, T., Tariq, M. U., Ahmad, W., Ouahada, K., & Hamam, H. (2025). A comprehensive review of large language models: Issues and solutions in learning environments. Discover Sustainability, 6(27).
Sharma, S., Mittal, P., Kumar, M., & Bhardwaj, V. (2025). The role of large language models in personalized learning: A systematic review of educational impact. Discover Sustainability, 6(243). https://doi.org/10.1007/s43621-025-01094-z
Song, X., & Tang, M. (2020). An empirical study on the impact of pre-interpreting preparation on business interpreting under Gile’s Efforts Model. Theory and Practice in Language Studies, 10(12), 1640-1650. http://dx.doi.org/10.17507/tpls.1012.19
Vieira, N. G. S. (2015). E-learning practices in translation and interpretation: Corpora as training platforms. Procedia—Social and Behavioral Sciences, 198, 157-164.
Wang, B. (2015). Bridging the gap between interpreting classrooms and real-world interpreting. International Journal of Interpreter Education, 7(1), 65-73.
Wiedenmayer, A. (2026). Artificial intelligence as a pedagogical tool for speech generation in conference interpreter training. [Journal details pending publication].
Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., Li, X., Jin, Y., & Gasevic, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90-112. https://doi.org/10.1111/bjet.13370
Yang, C., Chen, J., & Zou, D. (2025). Artificial intelligence in interpreting education curriculum: A Delphi study for interpreter competencies. International Journal of Education and Humanities (IJEH), 5(3), 387-403.
Yang, C., Hou, S., Zhao, M., Yan, J., & Chen, J. (2026). Translation students’ perceptions of the integration of artificial intelligence in translation education: A constructivist approach. Artificial Intelligence in Education, 2(2), 157-174. https://doi.org/10.1108/AHE-06-2025-0087
Yusuf, H., Money, A., & Daylamani-Zad, D. (2025). Pedagogical AI conversational agents in higher education: A conceptual framework and survey of the state of the art. Education and Information Technologies, 73, 815-874.
Zhao, N. (2022). Use of computer-assisted interpreting tools in conference interpreting: Training and practice during COVID-19. In K. Liu & A. K. F. Cheung (Eds.), Translation and interpreting in the age of COVID-19 (pp. 331-347). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-6680-4_17
Adamakis, M., & Rachiotis, T. (2025). Artificial intelligence in higher education: A state-of-the-art overview of pedagogical integrity, artificial intelligence literacy, and policy integration. Encyclopedia, 5(1), 180. https://doi.org/10.3390/encyclopedia5040180
Al-Suhaim, D. S. (2024). Exploring theoretical dimensions in interpreting studies: A comprehensive overview. Arab World English Journal for Translation & Literary Studies, 8(1), 15-43.
Bakar, S., & Tapsoba, R. (2026). Artificial intelligence in classroom teaching: Prospects, challenges and framework for responsibly orchestrated mediation. Social Science Chronicle, 6(1), 1-21. https://doi.org/10.56106/ssc.2026.002
Cai, R., Dong, Y., Zhao, N., & Lin, J. (2015). Factors contributing to individual differences in the development of consecutive interpreting competence for beginner student interpreters. The Interpreter and Translator Trainer, 9(1), 104-120.
Carrasco-Sáez, J. L., Contreras-Saavedra, C., San-Martín-Quiroga, S., Contreras-Saavedra, C. E., & Viveros-Muñoz, R. (2025). Analyzing higher education students’ prompting techniques and their impact on ChatGPT’s performance: An exploratory study in Spanish. Applied Sciences, 15(7651). https://doi.org/10.3390/app15147651
Chan, C. H. Y. (2013). From self-interpreting to real interpreting: A new web-based exercise to launch effective interpreting training. Perspectives: Studies in Translatology, 21(3), 358-377. https://doi.org/10.1080/0907676X.2012.657654
Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20(38). https://doi.org/10.1186/s41239-023-00408-3
Chan, V. (2023). Investigating the impact of a virtual reality mobile application on learners’ interpreting competence. Journal of Computer Assisted Learning, 1-17. https://doi.org/10.1111/jcal.12796
Chang, C.-C., & Wu, M. M.-C. (2017). From conference venue to classroom: The use of guided conference observation to enhance interpreter training. The Interpreter and Translator Trainer, 11(1), 21-42. https://doi.org/10.1080/1750399X.2017.1359759
Conde, J. M., & Chouc, F. (2019). Multilingual mock conferences: A valuable tool in the training of conference interpreters. The Interpreters’ Newsletter, 24, 1-17.
Cordero, J., Torres-Zambrano, J., & Cordero-Castillo, A. (2025). Integration of generative artificial intelligence in higher education: Best practices. Education Sciences, 15(1), 32. https://doi.org/10.3390/educsci15010032
Corpas Pastor, G. (2018). Tools for interpreters: The challenges that lie ahead. Current Trends in Translation Teaching and Learning E, 5, 157-182.
Corpas Pastor, G. (2020). Language technology for interpreters: The VIP Project. Proceedings of the 42nd Conference “Translating and the Computer” (TC42).
Cui, F., Li, D., & Zhuang, C. (2025). Introduction: Transforming translation education through artificial intelligence. The Interpreter and Translator Trainer, 19(3-4), 227-233. https://doi.org/10.1080/1750399X.2025.2561258
Djovcos, M., Klabal, O., & Sveda, P. (2023). Training interpreters: Old and new challenges. Bridge: Trends and Traditions in Translation and Interpreting Studies, 4(1), 1-12.
Fan, D. (2012). The development of expertise in interpreting through self-regulated learning for trainee interpreters [Doctoral dissertation]. University of Newcastle upon Tyne.
Frittella, F. M. (2021). Computer-assisted conference interpreter training: Limitations and future directions. Journal of Translation Studies, 2(2021), 103-142. https://doi.org/10.3726/JTS022021.6
Garcia-Penalvo, F. J. (2023). Generative artificial intelligence: Open challenges, opportunities, and risks in higher education. CEUR Workshop Proceedings, 3696, 4-15.
Gile, D. (1995). Basic concepts and models for interpreter and translator training. John Benjamins.
Gile, D. (1999). Testing the Effort Models’ tightrope hypothesis in simultaneous interpreting: A contribution. Hermes, Journal of Linguistics, 23, 153-172.
Gile, D. (2009). Basic concepts and models for interpreter and translator training (Rev. ed.). John Benjamins.
Gile, D. (2021). The Effort Models of interpreting as a didactic construct. In R. Munoz Martin et al. (Eds.), Advances in cognitive translation studies (pp. 139-153). Springer Nature Singapore.
Hatiarová, P. (2025). AI in interpreting training. L10N Journal, 1(4), 45-66.
Kalina, S. (2000). Interpreting competences as a basis and a goal for teaching. Fachhochschule Koln.
Kasneci, E., Sessler, K., Kuchemann, S., et al. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.
Li, X. (2015a). Mock conference as a situated learning activity in interpreter training: A case study of its design and effect as perceived by trainee interpreters. The Interpreter and Translator Trainer, 9(3), 323-341.
Li, X. (2015b). Putting interpreting strategies in their place: Justifications for teaching strategies in interpreter training. Babel, 61(2), 170-192. https://doi.org/10.1075/babel.61.2.02li
Li, X. (2018). Material development principles in undergraduate translator and interpreter training: Balancing between professional realism and classroom realism. The Interpreter and Translator Trainer, 12(4), 369-389.
Macnamara, B. N., Moore, A. B., Kegl, J. A., & Conway, A. R. A. (2011). Domain-general cognitive abilities and simultaneous interpreting skill. Interpreting, 13(1), 121-142.
Moser-Mercer, B. (2000/01). Simultaneous interpreting: Cognitive potential and limitations. Interpreting, 5(2), 83-94.
Moukatib, M., & Ben Seddik, A. (2026). The role of AI in translator training: Assessing AI’s influence on translation education and professional training. International Journal of Linguistics and Translation Studies, 7(1), 123-143. https://doi.org/10.36892/ijlts.v7i1.669
Munoz-Basols, J., Neville, C., Lafford, B. A., & Godev, C. (2023). Potentialities of applied translation for language learning in the era of artificial intelligence. Hispania, 106(2), 171-194.
Parrilla Gomez, L., & Postigo Pinazo, E. (2025). Artificial intelligence in the training of public service interpreters. Language & Communication, 103, 86-107.
Purba, S. W. D., Silitonga, B. N., & Yang, J. J. (2025). AI-assisted learning: A systematic review. Turkish Online Journal of Distance Education (TOJDE), 26(4), 77-94.
Qian, Y. (2025). Pedagogical applications of generative AI in higher education: A systematic review of the field. TechTrends, 69, 1105-1120.
Rybina, N. V., Koshil, N. Ye., & Hyryla, O. S. (2025). Artificial intelligence and translation in English language teaching: Opportunities and challenges. Medychna osvita [Medical Education], (2), 87-91. https://doi.org/10.11603/m.2414-5998.2025.2.15494
Sachtleben, A. (2015). Pedagogy for the multilingual classroom: Interpreting education. Translation & Interpreting: The International Journal of Translation and Interpreting Research, 7(2), 51-59.
Sandrelli, A., & de Manuel Jerez, J. (2007). The impact of information and communication technology on interpreter training: State-of-the-art and future prospects. The Interpreter and Translator Trainer, 1(2), 269-303.
Seeber, K. G. (2011). Cognitive load in simultaneous interpreting: Existing theories—New models. Interpreting, 13(2), 176-204. https://doi.org/10.1075/intp.13.2.02see
Seeber, K. G., & Arbona, E. (2020). What’s load got to do with it? A cognitive-ergonomic training model of simultaneous interpreting. The Interpreter and Translator Trainer, 14(3), 1-18. https://doi.org/10.1080/1750399X.2020.1839996
Seeber, K. G., & Kerzel, D. (2012). Cognitive load in simultaneous interpreting: Model meets data. International Journal of Bilingualism, 16(2), 228-242.
Serra, P., & Oliveira, A. (2025). AI-powered prompt engineering for Education 4.0: Transforming digital resources into engaging learning experiences. Education Sciences, 15(12), 1640. https://doi.org/10.3390/educsci15121640
Shahzad, T., Mazhar, T., Tariq, M. U., Ahmad, W., Ouahada, K., & Hamam, H. (2025). A comprehensive review of large language models: Issues and solutions in learning environments. Discover Sustainability, 6(27).
Sharma, S., Mittal, P., Kumar, M., & Bhardwaj, V. (2025). The role of large language models in personalized learning: A systematic review of educational impact. Discover Sustainability, 6(243). https://doi.org/10.1007/s43621-025-01094-z
Song, X., & Tang, M. (2020). An empirical study on the impact of pre-interpreting preparation on business interpreting under Gile’s Efforts Model. Theory and Practice in Language Studies, 10(12), 1640-1650. http://dx.doi.org/10.17507/tpls.1012.19
Vieira, N. G. S. (2015). E-learning practices in translation and interpretation: Corpora as training platforms. Procedia—Social and Behavioral Sciences, 198, 157-164.
Wang, B. (2015). Bridging the gap between interpreting classrooms and real-world interpreting. International Journal of Interpreter Education, 7(1), 65-73.
Wiedenmayer, A. (2026). Artificial intelligence as a pedagogical tool for speech generation in conference interpreter training. [Journal details pending publication].
Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., Li, X., Jin, Y., & Gasevic, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90-112. https://doi.org/10.1111/bjet.13370
Yang, C., Chen, J., & Zou, D. (2025). Artificial intelligence in interpreting education curriculum: A Delphi study for interpreter competencies. International Journal of Education and Humanities (IJEH), 5(3), 387-403.
Yang, C., Hou, S., Zhao, M., Yan, J., & Chen, J. (2026). Translation students’ perceptions of the integration of artificial intelligence in translation education: A constructivist approach. Artificial Intelligence in Education, 2(2), 157-174. https://doi.org/10.1108/AHE-06-2025-0087
Yusuf, H., Money, A., & Daylamani-Zad, D. (2025). Pedagogical AI conversational agents in higher education: A conceptual framework and survey of the state of the art. Education and Information Technologies, 73, 815-874.
Zhao, N. (2022). Use of computer-assisted interpreting tools in conference interpreting: Training and practice during COVID-19. In K. Liu & A. K. F. Cheung (Eds.), Translation and interpreting in the age of COVID-19 (pp. 331-347). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-6680-4_17
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